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EEG / MEG: Experimental Design & Preprocessing. Lena Kästner Thomas Ditye. Experimental Design Technology Signal Inferences Design Limitations Combined Measures. Preprocessing in SPM8 Data Conversion Montage Mapping Epoching Downsampling Filtering Artefact Removal Referencing.

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outline
Experimental Design

Technology

Signal

Inferences

Design

Limitations

Combined Measures

Preprocessing in SPM8

Data Conversion

Montage Mapping

Epoching

Downsampling

Filtering

Artefact Removal

Referencing

Outline
outline3
Experimental Design

Technology

Signal

Inferences

Design

Limitations

Combined Measures

Preprocessing in SPM8

Data Conversion

Montage Mapping

Epoching

Downsampling

Filtering

Artefact Removal

Referencing

Outline
eeg meg
Hans Berger (1924)

Hans Christian Orsted (1819)

David Cohen (1968)

Technology | Signal | Inferences | Design | Limitations | Combined Measures

EEG & MEG
electricity magnetism

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Electricity & Magnetism

apical dendrites of pyramidal cells act as dipoles

oscillations

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Oscillations
  • alpha (3 – 18Hz): awake, closed eyes
  • beta (18 – 30Hz):awake, alert; REM sleep
  • gamma (> 30Hz):memory (?)
  • delta (0.5 – 4 Hz):deep sleep
  • theta (4 – 8Hz):infants, sleeping adults
ep vs erp erf

Technology | Signal | Inferences | Design | Limitations | Combined Measures

EP vs. ERP / ERF
  • evoked potential
    • short latencies (< 100ms)
    • small amplitudes (< 1μV)
    • sensory processes
  • event related potential / field
    • longer latencies (100 – 600ms),
    • higher amplitudes (10 – 100μV)
    • higher cognitive processes
okay but what is it

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Okay, But What Is It?

average potential / field at the scalp relative to some specific event

Stimulus/Event

Onset

okay but what is it10

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Okay, But What Is It?

non-time locked activity (noise) lost via averaging

Averaging

evoked vs induced

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Evoked vs. Induced

(Hermann et al. 2004)

ers erd

Technology | Signal | Inferences | Design | Limitations | Combined Measures

ERS & ERD
  • event related synchronization
    • oscillatory power increase
    • associated with activity decrease?
  • event related desynchronization
    • oscillatory power increase
    • associated with activity increase?

long time windows, not phase-locked

inferences not based on prior knowledge
observe:

time course …

amplitude …

distribution across scalp …

differences in ERP

infer:

timing …

degree of engagement …

functional equivalence …

of underlying cognitive process

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Inferences Not Based On Prior Knowledge
inferences based on prior knowledge

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Inferences Based On Prior Knowledge

An “ERP component is scalp-recorded elec-trical activity that is generated in a given neuroanatomical module when a specific computational operation is performed.”

(Luck 2004, p. 22)

observed vs latent components

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Observed vs. Latent Components

Latent Components

Observed Waveform

OR

OR

many others…

observed vs latent components17

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Observed vs. Latent Components

Latent Components

Observed Waveform

design strategies

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Design Strategies
  • focus on specific, large, easily isolable component
  • use well-studied experimental manipulations
  • exclude secondary effects
  • avoid stimulus confounds (conduct control study)
  • vary conditions within rather than between trials
  • avoid behavioral confounds
sources of noise in eeg

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Sources of Noise in EEG
  • EEG activity not elicited by stimuli
    • e.g. alpha waves
  • trial-by-trial variations
  • articfactual bioelectric activity
    • eye blinks, eye movement, muscle activity, skin potentials
  • environmental electrical activity
    • e.g. from monitors
signal to noise

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Signal-to-Noise
  • noise said to average out
  • number of trials:
    • large component: 30 – 60 per condition
    • medium component: 150 – 200 per condition
    • small component: 400 – 800 per condition
    • double with children or psychiatric patients
limitations

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Limitations
  • ambiguous relation between observed ERP and latent components
  • signal distorted en route to scalp
    • arguably worse in EEG than MEG (head as “spherical conductor”)
  • MEG: application restrictions
    • patients with implants
  • poor localization (cf. “inverse problem”)
the best of all combining techniques

Technology | Signal | Inferences | Design | Limitations | Combined Measures

The Best of All – Combining Techniques?
  • MEG & EEG
    • simultaneous application
    • complementary information about current sources
    • joint approach to approximate inverse solution

… and how about fMRI?

the best of all combining techniques23

Technology | Signal | Inferences | Design | Limitations | Combined Measures

The Best of All – Combining Techniques?
  • EEG & fMRI
    • simultaneous application
    • e.g. spontaneous EEG-fMRI, evoked potential-fMRI
    • problem: scanner artifacts
the best of all combining techniques24

Technology | Signal | Inferences | Design | Limitations | Combined Measures

The Best of All – Combining Techniques?
  • MEG & fMRI
    • no simultaneous application
    • co registration (scalp-surface matching)
    • use structural scan: infer grey matter position to constrain inverse solution
    • run same experiment twice: use BOLD activation map to bias inverse solution
summary general design considerations

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Summary – General Design Considerations
  • large trial numbers, few conditions
  • avoid confounds
  • focus on specific effect, use established paradigm
  • take care when averaging
  • combined measures?
summary specific eeg considerations

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Summary – Specific EEG Considerations
  • amplifier and filter settings
  • sampling frequency
  • number, type, location of electrodes
  • reference electrodes
  • additional physiological measures?
summary specific meg considerations

Technology | Signal | Inferences | Design | Limitations | Combined Measures

Summary – Specific MEG Considerations
  • amplifier and filter settings
  • sampling frequency
  • equipment and participant compatible with MEG?
  • need to digitize 3D head or recording position?
outline28
Experimental Design

Technology

Signal

Inferences

Design

Limitations

Combined Measures

Preprocessing in SPM8

Data Conversion

Downsampling

Montage Mapping

Epoching

Filtering

Artefact Removal

Referencing

Outline
preprocessing

PREPROCESSING

Raw data to averaged ERP (EEG) or ERF (MEG) using SPM 8

slide30

Conversion of data

  • Convert data from its native machine-dependent format to MATLABbased SPM format

*.mat (data)

*.bdf

*.bin

*.eeg

  • ‘just read’ – quick and easy
  • define settings:
  • read data as ‘continuous’ or as ‘trials’
  • select channels
  • define file name

*.dat (other info)

slide31

128 channels

  • Unusually flat because data contain very low frequencies and baseline shifts
  • Viewing all channels only with a low gain
  • Intensity rescaling
slide32

Downsampling

  • Sampling frequency: number of samples per second taken from a continuous signal
  • SF should be greater than twice the maximum frequency of the signal being sampled
  • Data are usually acquired with a very high sampling rate (e.g. 2048 Hz)
  • Downsampling reduces the file size and speeds up the subsequent processing steps (e.g. 200 Hz)
slide33

Montage and referencing

  • Identify vEOG and hEOG channels, remove several channels that don’t carry EEG data;
  • Specify reference for remaining channels:
    • average reference: Output of all amplifiers are summed and averaged and the averaged signal is used as a common reference for each channel
    • single electrode reference: free from neural activity of interest (e.g. mastoid)
slide34

Epoching

  • Cut out chunks of continuous data (= single trials)
  • Specify time window associated with triggers [prestimulus time, poststimulus time]
  • Baseline-correction: automatic; the mean of the prestimulus time is subtracted from the whole trial
  • Segment length: at least 100 ms for baseline-correction; the longer the more artefacts
  • Padding: adds time points before and after each trial to avoid ‘edge effects’ when filtering

For multisubject/batch epoching in future

slide35

Filtering

  • EEG data consist of signal and noise
  • Some noise is sufficiently different in frequency content from the signal. It can be suppressed by attenuating different frequencies.
  • Non-neural physiological activity (skin/sweat potentials); noise from electrical outlets
  • SPM8: Butterworth filter
  • Any filter distorts at least some part of the signal
  • Gamma band activity occupies higher fequencies
  • compared to standard ERPs
slide37

Adding electrode locations

  • Not essential because SPM recognizes most common settings automatically (extended 10/20 system)
  • However, these are default locations based on electrode labels
  • Actual location might deviate from defaults
  • Individually measured electrode locations can be imported and used as templates

Change/review 2D display of electrode locations

1. Load file

2. Change/review channel assignments

  • 3. Set sensor positions
  • Assign defaults
  • From .mat file
  • From user-written locations file
slide38

Artefact Removal

  • Artefacts: Eye movements, eye blinks, head movements, sweating, ‘boredom’ (alpha waves), …
  • It’s best to avoid artefacts in the first place
      • Blinking: avoid contact lenses; have short blocks and blink breaks
      • EMG: make subjects relax, shift position, open mouth slightly
      • Alpha waves: more runs, shorter length; variable ISI; talk to subjects
  • Removal
      • Hand-picked
      • Automatic SPM functions:
        • Thresholding (e.g. 200 μV): 1st – bad channels, 2nd – bad trials
        • No change to data, just tagged
        • Robust averaging: estimates weights (0-1) indicating how artefactual a trial is
slide39

Excursus: Concurrent EEG/fMRI

  • MR gradient artefact:
    • Very consistent because it’s caused by the scanner
    • Averaged artefact waveform is created on the basis of event markers
    • Subtract template
  • Ballistocardogram (BCG) artefacts:
    • Caused my small movements of the leads and electrodes following cardiac pulsation
    • Much less consistent
    • PCA: Definition of a basis function by running PCA, fitting, subtracting from data
  • SPM8 extension: FAST; http://www.montefiore.ulg.ac.be/~phillips/FAST.html
slide40

Signal averaging

  • S/N ratio increases as a function of the square root of the number of trials
  • It’s better to decrease sources of noise than to increase number of trials
references
References
  • Ashburner, J. et al. (2010). SPM8 Manual. http://www.fil.ion.ucl.ac.uk/spm/
  • Hermann, C. et al. (2004). Cognitive functions of gammaband activity: memory match and utilization. Trends in Cognitive Science, 8(8), 347-355.
  • Luck, R. L. (2005). Ten simple rules for designing ERP experiments. In T. C. Handy (Ed.), Event-related potentials: a methods handbook. Cambridge, MA: MIT Press.
  • Otten, L. J. & Rugg, M. D. (2005). Interpreting event-related brain potentials. In T. C. Handy (Ed.), Event-related potentials: a methods handbook. Cambridge, MA: MIT Press.
  • Rippon, G. (2006). Electroencephalography. In C. Senior, T. Russell, & M. S. Gazzaniga (Eds.), Methods in Mind.
  • Rugg, M.D. & Curran, T. (2007). Event-related potentials and recognition memory. Trends in Cognitive Science, 11(6), 251-257.
  • Singh, K. D. (2006). Magnetoencephalography. In C. Senior, T. Russell, & M. S. Gazzaniga (Eds.), Methods in Mind.
  • MfD slides from previous years(with special thanks to Matthias Gruber and Nick Abreu for their EEG signal illustrations)
thank you

Thank You!

… and next week: contrasts, inference and source localization